P-1954. Wastewater-Based Surveillance More Accurately Describes Disease Burden Of COVID-19 In Communities with Less Than 60,000 Inhabitants – An Ontario-Wide Study
Bibliographic record
Abstract
Abstract Background The emergence of COVID-19 in Canada prompted extensive efforts in population-wide surveillance, including wastewater-based surveillance (WBS). WBS offers insights into the prevalence of infectious disease burden, however, reasons for poor correlation between hospital admissions and wastewater viral signal in some locations across Ontario remain unclear, while others demonstrate remarkable correlations. This study aims to elucidate the parameters influencing hospitalization-WBS correlation quality to guide infectious disease research and surveillance.Figure 1:Geographic overview of sampling locations in Ontario, Canada Methods Wastewater viral signals and daily hospital admissions geospatially linked with the surveyed sewersheds, were obtained from the Ontario Wastewater Surveillance Initiative. The study included 94 sampling sites (Figure 1), excluding those with inadequate sampling frequency. Spearman’s rank correlation coefficient (ρ) was calculated between hospital admissions and wastewater signals for all included sites during the Omicron BA.1 and BA.2 waves from Nov. 1st,2021 to Jun. 30th, 2022. Regression trees were constructed using the R packages “rpart” and “rpart.plot” to identify factors influencing hospitalization-WBS ρ, including population size, hospital admissions range, proportion of zero admissions, and site isolation status.Figure 2:Regression trees representing a classification model for evaluating the correlation quality between hospitalizations and wastewater signal during the Omicron BA.1 wave – coinciding with a period of waned vaccination immunity (A) and the Omicron BA.2 wave – coinciding with a period of significant vaccination immunity (B). Results Regression tree analysis identified critical population size thresholds influencing hospitalization-WBS ρ: populations under 66,000 exhibited weaker ρ (ρ > 0.650) during Omicron BA.1, and under 187,000 during the BA.2 wave (Figure 3). Larger population areas exhibited stronger hospitalization-WBS ρ, suggesting both are reliable infectious disease burden indicators, influenced by differences in community immunity dynamics between the two waves. Conclusion Populations under 66,000 during a period of waned vaccine immunity and under 187,000 during a period of significant vaccine immunity exhibit distinct relationship between hospital admissions and wastewater signals. These thresholds mark key sizes of communities where the efficacy of wastewater surveillance becomes more pronounced, emphasizing its utility in smaller populations where clinical surveillance of infectious diseases may be limited. Disclosures All Authors: No reported disclosures
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".